{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:CYANJ6VOKU6URH3EYTOOHRBCSX","short_pith_number":"pith:CYANJ6VO","canonical_record":{"source":{"id":"1907.11049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-25T13:45:48Z","cross_cats_sorted":[],"title_canon_sha256":"910ca5ecd8ff4fb07ecfb4f16ddc63e8d01fec6b387e377c4ac6ed41d58b0678","abstract_canon_sha256":"f1c1b2a2f2b73d565b310e2bce9e791d7bca98363ed78bc0ac07eb7f735fd901"},"schema_version":"1.0"},"canonical_sha256":"1600d4faae553d489f64c4dce3c42295fb474de79f9533bec9c126e95d07756d","source":{"kind":"arxiv","id":"1907.11049","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11049","created_at":"2026-05-17T23:39:33Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11049v1","created_at":"2026-05-17T23:39:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11049","created_at":"2026-05-17T23:39:33Z"},{"alias_kind":"pith_short_12","alias_value":"CYANJ6VOKU6U","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"CYANJ6VOKU6URH3E","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"CYANJ6VO","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:CYANJ6VOKU6URH3EYTOOHRBCSX","target":"record","payload":{"canonical_record":{"source":{"id":"1907.11049","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-25T13:45:48Z","cross_cats_sorted":[],"title_canon_sha256":"910ca5ecd8ff4fb07ecfb4f16ddc63e8d01fec6b387e377c4ac6ed41d58b0678","abstract_canon_sha256":"f1c1b2a2f2b73d565b310e2bce9e791d7bca98363ed78bc0ac07eb7f735fd901"},"schema_version":"1.0"},"canonical_sha256":"1600d4faae553d489f64c4dce3c42295fb474de79f9533bec9c126e95d07756d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:33.648238Z","signature_b64":"j+ZFz90V0kiDbduAAYEqEqJ6ParqGkdVbNCSf+wmb2eH1wxXG2h0XfRzUNUEPFXDWSXybUZ+hHpGU8bgFqxcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1600d4faae553d489f64c4dce3c42295fb474de79f9533bec9c126e95d07756d","last_reissued_at":"2026-05-17T23:39:33.647686Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:33.647686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.11049","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jniVxDF9pQrAcsbggp/fo9dRYCSXIUsSapKo6mzfhQLZQXkzkr+jIR2mEE247lRZ47FSilMPG3yFXPi4JqynBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:24:30.388746Z"},"content_sha256":"19c37682957f9b69ee1c14a78817105a7d0f14304bdb72061fe960bd9547649d","schema_version":"1.0","event_id":"sha256:19c37682957f9b69ee1c14a78817105a7d0f14304bdb72061fe960bd9547649d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:CYANJ6VOKU6URH3EYTOOHRBCSX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Christoph Teichmann, Chunyang Xiao, Konstantine Arkoudas","submitted_at":"2019-07-25T13:45:48Z","abstract_excerpt":"While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11049","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ijzfyqeC0LJp/1dd+UF/OS4fTx2KKJc2tGRuHVtkIA5zouq4QZpLoXoDb8uwfd+GZTMGKowycWphWiQxDHapAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:24:30.389380Z"},"content_sha256":"704cd7bacb537b030e0376056b21e51b425c3f4f176190d97e28278e95aec810","schema_version":"1.0","event_id":"sha256:704cd7bacb537b030e0376056b21e51b425c3f4f176190d97e28278e95aec810"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CYANJ6VOKU6URH3EYTOOHRBCSX/bundle.json","state_url":"https://pith.science/pith/CYANJ6VOKU6URH3EYTOOHRBCSX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CYANJ6VOKU6URH3EYTOOHRBCSX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T21:24:30Z","links":{"resolver":"https://pith.science/pith/CYANJ6VOKU6URH3EYTOOHRBCSX","bundle":"https://pith.science/pith/CYANJ6VOKU6URH3EYTOOHRBCSX/bundle.json","state":"https://pith.science/pith/CYANJ6VOKU6URH3EYTOOHRBCSX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CYANJ6VOKU6URH3EYTOOHRBCSX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:CYANJ6VOKU6URH3EYTOOHRBCSX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f1c1b2a2f2b73d565b310e2bce9e791d7bca98363ed78bc0ac07eb7f735fd901","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-25T13:45:48Z","title_canon_sha256":"910ca5ecd8ff4fb07ecfb4f16ddc63e8d01fec6b387e377c4ac6ed41d58b0678"},"schema_version":"1.0","source":{"id":"1907.11049","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11049","created_at":"2026-05-17T23:39:33Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11049v1","created_at":"2026-05-17T23:39:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11049","created_at":"2026-05-17T23:39:33Z"},{"alias_kind":"pith_short_12","alias_value":"CYANJ6VOKU6U","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"CYANJ6VOKU6URH3E","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"CYANJ6VO","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:704cd7bacb537b030e0376056b21e51b425c3f4f176190d97e28278e95aec810","target":"graph","created_at":"2026-05-17T23:39:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"While sequence-to-sequence (seq2seq) models achieve state-of-the-art performance in many natural language processing tasks, they can be too slow for real-time applications. One performance bottleneck is predicting the most likely next token over a large vocabulary; methods to circumvent this bottleneck are a current research topic. We focus specifically on using seq2seq models for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance semantics. By developing a generic approach for restricting the predictions of a seq2seq model to g","authors_text":"Christoph Teichmann, Chunyang Xiao, Konstantine Arkoudas","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-25T13:45:48Z","title":"Grammatical Sequence Prediction for Real-Time Neural Semantic Parsing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11049","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:19c37682957f9b69ee1c14a78817105a7d0f14304bdb72061fe960bd9547649d","target":"record","created_at":"2026-05-17T23:39:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f1c1b2a2f2b73d565b310e2bce9e791d7bca98363ed78bc0ac07eb7f735fd901","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-07-25T13:45:48Z","title_canon_sha256":"910ca5ecd8ff4fb07ecfb4f16ddc63e8d01fec6b387e377c4ac6ed41d58b0678"},"schema_version":"1.0","source":{"id":"1907.11049","kind":"arxiv","version":1}},"canonical_sha256":"1600d4faae553d489f64c4dce3c42295fb474de79f9533bec9c126e95d07756d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1600d4faae553d489f64c4dce3c42295fb474de79f9533bec9c126e95d07756d","first_computed_at":"2026-05-17T23:39:33.647686Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:33.647686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j+ZFz90V0kiDbduAAYEqEqJ6ParqGkdVbNCSf+wmb2eH1wxXG2h0XfRzUNUEPFXDWSXybUZ+hHpGU8bgFqxcDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:33.648238Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.11049","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19c37682957f9b69ee1c14a78817105a7d0f14304bdb72061fe960bd9547649d","sha256:704cd7bacb537b030e0376056b21e51b425c3f4f176190d97e28278e95aec810"],"state_sha256":"b590b0141e425248374beafc374a2d79f41d8538961040fd01dd427fc05a075a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hq6py0kuMrYuLalEk0BdU4UsIDu1pdMZT9KDOjU2BXm3RldYbphWS16VUxchSaF43NIThIjJ0bdpFGWniT82BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:24:30.393302Z","bundle_sha256":"ed3eaf996c8ff514459d16a3acedf6d6e349b651435e5344606fa5d647efed54"}}